Many industries require that vast amounts of data pertaining to a particular system be monitored and analyzed in order to make split-second assessments concerning the condition of the system. For example, physicians and anesthesiologists routinely monitor more than thirty physiological parameters (e.g., heart rate, blood pressure, cardiac output, etc.) when treating patients in intensive care units, operating rooms, and emergency rooms. Also, financial advisors and analysts in the course of their work frequently check many parameters that can influence stock prices (e.g., closing price, 52-week high and low, dividends, yield, change, previous day high and low, etc.) in order to appropriately advise their clients. Further, control room operators, such as in industrial, power plant, and aviation control rooms, monitor a variety of outputs to insure that the system being monitored is functioning properly.
Often, misdiagnosis of the condition of the system occurs because of the sheer volume of the data to be monitored. For example, in the field of anesthesiology, anesthesiologists are surrounded by multiple vital sign monitors that display many data elements and can generate a myriad of alarms. In the noisy and congested atmosphere surrounding an operation on a patient or in the initial period after the operation when a large number of patients may be monitored by a few persons, busy physicians and nurses can miss physiological changes in the patient that are material to the well being of the patient. In this regard, it is estimated that in the United States, between 2,000 and 10,000 patients die each year from anesthesia related accidents. It is believed that many of these accidents could be avoided by transforming the plethora of data currently provided to the physician by monitors into a more useful form that would provide an earlier indication of physiological changes in the condition of the patient.
Monitoring systems have been developed to assist users in processing vast amounts of data. For example, in the medical profession, a patient monitoring system has been described wherein a plurality of medical parameters are measured and transformed to provide a danger level associated with each parameter. The highest danger level is selected to represent the status of the system. The transformation of the medical parameters is performed using a function exhibiting a maximum slope for parameter values near the homeostasis level for that parameter. As the system is extremely sensitive to small changes in each parameter about the homeostasis level of each parameter, the system can lead to false warnings. Also, the measurement of the highest danger level can have limited usefulness as an indicator of the status of the system or patient. More frequently, the user can better assess the status of the system or patient if the user is provided with information about the parameters not registering the highest danger or critical level and information about the parameter at the highest danger or critical level before that parameter reached the critical level.
In light of the above, it would be advantageous to provide an apparatus and method for monitoring a system wherein an overwhelmingly large amount of data is consolidated to provide the user with a manageable amount of information to assess the condition of the system and changes in most, if not all, of a set of measured parameters associated with the system. Preferably, the apparatus and method is responsive to the requirements of the user and the specific system being monitored. In addition, the system and method should minimize the number of false warnings and be rapid enough to provide information in a time frame that is required by the user.